Block Size

Block size, the number of data units processed together, is a crucial parameter influencing efficiency and accuracy across diverse applications. Current research focuses on optimizing block size for improved performance in areas such as wireless network resource allocation (using diffusion models and deep reinforcement learning), homomorphic encryption for private inference (leveraging block circulant transformations), and data compression/quantization (exploring optimal block sizes for various data distributions and minimizing reconstruction error). These optimizations significantly impact fields ranging from high-performance computing, where efficient data partitioning is critical, to the design of energy-efficient communication systems and the development of novel numerical formats for deep learning.